R Notebook

Loading requierd libraries

(library(ggplot2))
(library(tidyr))
(library(dplyr))
(library(readxl))
(library(gridExtra))
(library(plotly))

Getting the complete raw data

raw.data <- read.csv("data/Fifa19C.csv",stringsAsFactors = F)

Selecting only the required columns from the complete dataframe

data <- raw.data %>% select(ID,Name,Age,Nationality,Overall,Potential,Club,Value,Wage,Preferred.Foot,International.Reputation,Weak.Foot,Skill.Moves,Position,Jersey.Number,Height,Weight,Release.Clause)

Converting categorical values to factors

toNumberCurrency <- function(vector) {
    vector <- as.character(vector)
    #replace '???' with 'Euro sign'
    vector <- gsub("(€|,)","", vector) 
    result <- as.numeric(vector)
   
    k_positions <- grep("K", vector)
    result[k_positions] <- as.numeric(gsub("K","",vector[k_positions])) * 1000
  
    m_positions <- grep("M", vector)
    result[m_positions] <- as.numeric(gsub("M","",vector[m_positions])) * 1000000
    
    return(result)
}
data$Wage <- toNumberCurrency(data$Wage) 

NAs introduced by coercion

data$Value <- toNumberCurrency(data$Value)

NAs introduced by coercion

data$Release.Clause <- toNumberCurrency(data$Release.Clause)

NAs introduced by coercion

Converting height to centimeters

l <- strsplit(data$Height,"'",fixed = T)
data$Height <- sapply(l, function(x) sum(as.numeric(x)*c(30.4,2.54)))

Removing lbs from the weight column

data$Weight <- gsub("[a-zA-Z ]", "", data$Weight)
data$Weight <- as.numeric(data$Weight)

Replace specefic positions of players with more general positions

data$Position <- if_else(data$Position=="ST" | data$Position=="RS" | data$Position=="LS","ST",data$Position)
data$Position <- if_else(data$Position=="LF" | data$Position=="LW","LW",data$Position)
data$Position <- if_else(data$Position=="RF" | data$Position=="RW","RW",data$Position)
data$Position <- if_else(data$Position=="CM" | data$Position=="RCM" | data$Position=="LCM","CM",data$Position)
data$Position <- if_else(data$Position=="CB" | data$Position=="RCB" | data$Position=="LCB","CB",data$Position)
data$Position <- if_else(data$Position=="CDM" | data$Position=="LDM" | data$Position=="RDM","CDM",data$Position)
data$Position <- if_else(data$Position=="CAM" | data$Position=="LAM" | data$Position=="RAM","CAM",data$Position)
head(data)

Total players based on position

Getting top players using order

top.players <- data[order(data$Overall,decreasing = T),]

top.25 <- head(top.players,25)

Jersey numbers for the top 25 players

pl <- ggplot(top.25,aes(Jersey.Number)) + geom_bar() + theme_minimal() 
ggplotly(pl)

Ages of top 25 players

pl <- ggplot(top.25,aes(Age)) + geom_histogram(binwidth = 1) + theme_minimal() 
ggplotly(pl)

Top 25 based on countries

Top 25 by position

pl <- ggplot(top.25,aes(x = Position,fill = Name)) + geom_bar() + theme_minimal() 
ggplotly(pl)

Getting the top potential players with age < 25

top.50.potential <- head(top.potential,50)

Clubs of top 50 potential players

pl <- ggplot(top.50.potential,aes(x=Nationality)) + geom_bar(aes(fill=Name)) + theme_minimal() + theme(axis.text.x = element_text(angle = 45,hjust = 1))
ggplotly(pl)

top.25.and.top.50.potential <- rbind(top.25,top.50.potential)

Top 50 players based on Club